Senior AI Platform Developer

Our SaaS client is looking to hire an AI Platform Developer to join their team on a contract to hire basis. These people will need to commute onsite 4 days a week to Herndon, VA.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

7-10 years experience as an AI Platform Developer

Programming Languages: Mastery of Python is essential, with R, Java, and C++ also being highly valuable.

Machine Learning (ML) & Deep Learning (DL): You'll need a deep understanding of ML concepts (supervised, unsupervised, reinforcement learning) and neural network architectures like CNNs and RNNs.

AI/ML Frameworks and Libraries: Proficiency is required in tools like TensorFlow, PyTorch, Keras, and scikit-learn.

Data Science and Analysis: Skills in data acquisition, cleaning, preprocessing, and feature engineering are crucial, along with knowledge of SQL and NoSQL databases.

Big Data Technologies: Familiarity with platforms like Apache Spark and OpenSearch is often necessary for handling large-scale data.

Mathematics and Statistics: A strong foundation in linear algebra, calculus, probability, and statistics is fundamental.

Natural Language Processing (NLP): For language-based AI, expertise in NLP techniques and libraries such as NLTK, spaCy, and Hugging Face Transformers is key.

Cloud Computing and MLOps: Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps principles is vital for deploying and managing AI models.